aBasque Centre for Climate Change (BC3), Sede
Building, Campus EHU/UPV, Leioa, Bizkaia, Spain.
bInstituto Español de Oceanografía (IEO-CSIC), Centro
Oceanográfico de Cádiz, Puerto pesquero, Muelle de Levante s/n, Apdo.
2609, 11006 Cádiz, Spain.
Small and mid-size pelagic fish occupy intermediate levels in the marine trophic webs where they play an essential role (Cury et al., 2000). These fish act as links between the upper and lower trophic levels, capturing energy from lower levels and making it available to higher trophic levels (Morello & Arneri, 2009). Global captures of finfish, the taxon where small and mid-size pelagic fish are included, account for the 85% of the marine captures, with small pelagics as the main group (FAO, 2024a).
According to the economic importance of Engraulis encrasicolus, the species under study, it accounted for approximately 4% of the total european countries fisheries in 2022 (FAO, 2024b). Concerning Spain and Portugal, Engraulis encrasicolus covers more than 5% and 2% of the total fisheries, respectively (FAO, 2024b). Therefore, the knowledge and correct management of the Engraulis encrasicolus fishery is of utmost importance for the fishing sector of both countries.
In this study we apply the Stocastic Surplus Production Model in Continuous Time (SPiCT) with 4 different model configurations to evaluate the Engraulis encrasicolus stock status in ICES Division 9a South. Model results allow us to establish reference points for Maximum Sustainable Yield, Biomass at Maximum Sustainable Yield and Fishing Mortality at Maximum Sustainable Yield as well as to determine how model configuration could affect the estimates and model robustness.
We used data from commercial landings as catch obervations and independent scientific survey data as exploitable biomass indices. In this sense, we used quarterly commercial landings data comprised from 1989 to 2023 and yearly data from PELAGO (1999-2023), ECOCADIZ (2004-2023), ECOCADIZ-RECLUTAS (2012-2023) and BOCADEVA (2005-2023) surveys. We obtained the corresponding exploitable biomass index for each period and survey considering the minimum length observed in the landings during that period. In addition, we added as much uncertainty as possible, without compromising the stability of the model, to the 2012 estimate of the ECOCADIZ-RECLUTAS survey since it was only sampled in Spanish waters.
Stocastic Surplus Production Model in Continuos Time (Pedersen & Berg, 2017) is a model that has been widely used in data-limited situations (i.e. Bluemel et al., 2021; González Herraiz et al., 2023; Soto et al., 2023). This stochastic state-space model aggregates biomass across size and age groups, using the equations reported by Pella and Tomlinson (1969), providing stock status estimates and reproducing population dynamics (Derhy et al., 2022). By relaxing the common assumption that catches are known without error, SPiCT permits to assess fish stock status with a more realistic quantification of uncertainty (Pedersen & Berg, 2017), allowing for a broader perspective of the stock situation and a better understanding of the risks associated with management decisions.
We tested the SPiCT model with 4 different configurations of the input data:
plotspict.data(sc_1_data, qlegend = TRUE)
plotspict.data(sc_2_data, qlegend = TRUE)
plotspict.data(sc_3_data, qlegend = TRUE)
plotspict.data(sc_4_data, qlegend = TRUE)
We implemented the model and scenarios using the SPiCT package (Pedersen & Berg, 2017) from R (R Core Team, 2024) and the default priors.
We obtained two types of results: a) model parameter estimates, reference points & state estimations and b) model diagnostics for model acceptance.
The model obtained acceptable uncertainty levels and an estimated exploitable biomass of 2219.43 tonnes and a fising mortality of 3.25 in 2023. Predicted catchabilities were 6.29, 8.75 and 5.31 for PELAGO, ECOCADIZ and ECOCADIZ-RECLUTAS, respectively. Additionally, Kobe plot shows that the stock has suboptimal biomass estimates as well as lower fishing mortality than fishing mortality at Maximum Sustainable Yield (MSY).
res_sc_1 <- fit.spict(sc_1_data)
summary(res_sc_1)
Convergence: 0 MSG: relative convergence (4)
Objective function at optimum: 217.9455143
Euler time step (years): 1/16 or 0.0625
Nobs C: 140, Nobs I1: 21, Nobs I2: 14, Nobs I3: 10
Priors
logn ~ dnorm[log(2), 2^2]
logalpha ~ dnorm[log(1), 2^2]
logbeta ~ dnorm[log(1), 2^2]
Model parameter estimates w 95% CI
estimate cilow ciupp log.est
alpha1 0.1472304 0.0251668 8.613235e-01 -1.9157567
alpha2 0.1707301 0.0319501 9.123218e-01 -1.7676714
alpha3 0.3986602 0.1161241 1.368622e+00 -0.9196458
beta 1.6935246 0.8889400 3.226343e+00 0.5268119
r 4.2377969 1.1869716 1.513004e+01 1.4440435
rc 5.9281435 2.5109657 1.399577e+01 1.7797111
rold 9.8617303 3.7699535 2.579706e+01 2.2886616
m 7830.0805345 5311.6801952 1.154252e+04 8.9657281
K 6069.6185357 2297.6358114 1.603399e+04 8.7110510
q1 6.2913041 2.3604551 1.676817e+01 1.8391684
q2 8.7530111 2.3391956 3.275280e+01 2.1693978
q3 5.3056998 1.5223404 1.849156e+01 1.6687817
n 1.4297214 0.8350964 2.447745e+00 0.3574796
sdb 1.1780900 0.6023364 2.304188e+00 0.1638945
sdf 0.3449096 0.1755653 6.775977e-01 -1.0644729
sdi1 0.1734506 0.0387140 7.771132e-01 -1.7518622
sdi2 0.2011354 0.0431693 9.371339e-01 -1.6037769
sdi3 0.4696576 0.2032237 1.085397e+00 -0.7557513
sdc 0.5841129 0.4477046 7.620825e-01 -0.5376610
phi1 3.8831595 1.8088397 8.336243e+00 1.3566491
phi2 11.3419666 6.3157292 2.036823e+01 2.4285097
phi3 11.4554653 5.8256527 2.252583e+01 2.4384669
Deterministic reference points (Drp)
estimate cilow ciupp log.est
Bmsyd 2641.663622 1134.936110 6148.704437 7.879164
Fmsyd 2.964072 1.255483 6.997883 1.086564
MSYd 7830.080534 5311.680195 11542.517419 8.965728
Stochastic reference points (Srp)
estimate cilow ciupp log.est rel.diff.Drp
Bmsys 2435.613471 1380.317367 4297.716688 7.797954 -0.08459887
Fmsys 4.224397 2.676875 6.666552 1.440876 0.29834440
MSYs 10548.688285 5533.814449 20108.159670 9.263757 0.25771998
States w 95% CI (inp$msytype: s)
estimate cilow ciupp log.est
B_2023.94 2219.4307185 623.5240116 7900.052961 7.7050060
F_2023.94 3.2466487 1.0825588 9.736864 1.1776233
B_2023.94/Bmsy 0.9112409 0.2854531 2.908919 -0.0929479
F_2023.94/Fmsy 0.7685473 0.2309132 2.557953 -0.2632532
Predictions w 95% CI (inp$msytype: s)
prediction cilow ciupp log.est
B_2025.00 2280.2387517 396.1363036 13125.504321 7.7320354
F_2025.00 3.2466502 0.8842016 11.921192 1.1776238
B_2025.00/Bmsy 0.9362072 0.1787497 4.903415 -0.0659185
F_2025.00/Fmsy 0.7685476 0.1914702 3.084895 -0.2632528
Catch_2024.00 6151.1230310 2718.4802992 13918.186037 8.7243900
E(B_inf) 3001.0968823 NA NA 8.0067331
plot(res_sc_1)
According to the diagnostic checklist for model acceptance, the model meets all requirements except normality of catch residuals.
# if 0 => OK
res_sc_1$opt$convergence
[1] 0
# if TRUE => OK
all(is.finite(res_sc_1$sd))
[1] TRUE
res_sc_1 <- calc.osa.resid(res_sc_1)
plotspict.diagnostic(res_sc_1)
# if -0.2 < mohns_rho < 0.2 => OK
retro_sc_1 <- retro(res_sc_1, nretroyear = 3)
plotspict.retro(retro_sc_1)
FFmsy BBmsy
0.004889144 0.010451340
plotspict.retro.fixed(retro_sc_1)
# if between 0.1 and 0.9 => OK
calc.bmsyk(res_sc_1)
[1] 0.4352273
plotspict.production(res_sc_1)
# Main variance paramterers (logsdb, logsdc, logsdi, logsdf) should not be unreallistically high:
get.par("logsdb", res_sc_1)
ll est ul sd cv
logsdb -0.5069392 0.1638945 0.8347282 0.3422684 2.088346
get.par("logsdc", res_sc_1)
ll est ul sd cv
logsdc -0.8036216 -0.537661 -0.2717005 0.1356966 -0.2523832
get.par("logsdi", res_sc_1)
ll est ul sd cv
logsdi -3.251555 -1.7518622 -0.25216928 0.7651635 -0.4367715
logsdi -3.142625 -1.6037769 -0.06492909 0.7851409 -0.4895574
logsdi -1.593448 -0.7557513 0.08194539 0.4274041 -0.5655354
get.par("logsdf", res_sc_1)
ll est ul sd cv
logsdf -1.739744 -1.064473 -0.3892015 0.3445326 -0.3236649
calc.om(res_sc_1) # if order of magnitude < 2 => OK)
lower est upper CI range order magnitude
B/Bmsy 0.29 0.91 2.91 2.62 1
F/Fmsy 0.06 0.21 0.73 0.67 1
check_sc_1$check.ini$resmat # Trials that converged should have same or similar estimates.
Distance m K q q q n sdb sdf sdi sdi
Basevec 0.00 7830.08 6069.62 6.29 8.75 5.31 1.43 1.18 0.34 0.17 0.20
Trial 1 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 2 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 3 0.01 7830.08 6069.61 6.29 8.75 5.31 1.43 1.18 0.34 0.17 0.20
Trial 4 0.02 7830.08 6069.64 6.29 8.75 5.31 1.43 1.18 0.34 0.17 0.20
Trial 5 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 6 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 7 0.02 7830.08 6069.64 6.29 8.75 5.31 1.43 1.18 0.34 0.17 0.20
Trial 8 0.00 7830.08 6069.62 6.29 8.75 5.31 1.43 1.18 0.34 0.17 0.20
Trial 9 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 10 7409.58 13486.64 10855.55 3.16 3.55 2.21 2.41 1.20 0.44 0.18 0.23
Trial 11 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 12 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 13 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 14 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 15 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 16 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 17 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 18 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 19 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 20 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 21 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 22 7409.08 13486.17 10855.34 3.16 3.55 2.21 2.41 1.20 0.44 0.18 0.23
Trial 23 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 24 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 25 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 26 0.09 7830.09 6069.71 6.29 8.75 5.31 1.43 1.18 0.34 0.17 0.20
Trial 27 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 28 0.08 7830.13 6069.56 6.29 8.75 5.31 1.43 1.18 0.34 0.17 0.20
Trial 29 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 30 0.00 NA NA NA NA NA NA NA NA NA NA
sdi sdc phi phi phi
Basevec 0.47 0.58 3.88 11.34 11.46
Trial 1 NA NA NA NA NA
Trial 2 NA NA NA NA NA
Trial 3 0.47 0.58 3.88 11.34 11.46
Trial 4 0.47 0.58 3.88 11.34 11.46
Trial 5 NA NA NA NA NA
Trial 6 NA NA NA NA NA
Trial 7 0.47 0.58 3.88 11.34 11.46
Trial 8 0.47 0.58 3.88 11.34 11.46
Trial 9 NA NA NA NA NA
Trial 10 0.54 0.62 5.78 15.82 9.10
Trial 11 NA NA NA NA NA
Trial 12 NA NA NA NA NA
Trial 13 NA NA NA NA NA
Trial 14 NA NA NA NA NA
Trial 15 NA NA NA NA NA
Trial 16 NA NA NA NA NA
Trial 17 NA NA NA NA NA
Trial 18 NA NA NA NA NA
Trial 19 NA NA NA NA NA
Trial 20 NA NA NA NA NA
Trial 21 NA NA NA NA NA
Trial 22 0.54 0.62 5.78 15.82 9.10
Trial 23 NA NA NA NA NA
Trial 24 NA NA NA NA NA
Trial 25 NA NA NA NA NA
Trial 26 0.47 0.58 3.88 11.34 11.46
Trial 27 NA NA NA NA NA
Trial 28 0.47 0.58 3.88 11.34 11.46
Trial 29 NA NA NA NA NA
Trial 30 NA NA NA NA NA
In this second configuration of the model, the uncertainty levels were also acceptable and the estimated exploitable biomass was 2217.19 tonnes and the fishing mortality was 3.25 for 2023. Predicted catchabilities were estimated at 6.30, 8.77 and 5.31 for PELAGO, ECOCADIZ and ECOCADIZ-RECLUTAS, respectively. Additionally, Kobe plot also shows that the stock has suboptimal biomass estimates as well as lower fishing mortality than fishing mortality at MSY.
res_sc_2 <- fit.spict(sc_2_data)
summary(res_sc_2)
Convergence: 0 MSG: relative convergence (4)
Objective function at optimum: 217.9459292
Euler time step (years): 1/16 or 0.0625
Nobs C: 140, Nobs I1: 21, Nobs I2: 14, Nobs I3: 10
Priors
logn ~ dnorm[log(2), 2^2]
logalpha ~ dnorm[log(1), 2^2]
logbeta ~ dnorm[log(1), 2^2]
Model parameter estimates w 95% CI
estimate cilow ciupp log.est
alpha1 0.1469549 0.0250799 8.610786e-01 -1.9176298
alpha2 0.1704595 0.0318803 9.114243e-01 -1.7692575
alpha3 0.3977649 0.1155602 1.369130e+00 -0.9218942
beta 1.6934230 0.8885506 3.227370e+00 0.5267520
r 4.2466456 1.1853627 1.521391e+01 1.4461294
rc 5.9369191 2.5087730 1.404950e+01 1.7811903
rold 9.8624104 3.7767789 2.575399e+01 2.2887306
m 7831.8636127 5312.1014581 1.154686e+04 8.9659558
K 6060.4068303 2290.4188151 1.603573e+04 8.7095322
q1 6.3010912 2.3609741 1.681668e+01 1.8407228
q2 8.7714196 2.3392162 3.289042e+01 2.1714987
q3 5.3134927 1.5229434 1.853858e+01 1.6702494
n 1.4305890 0.8348883 2.451328e+00 0.3580863
sdb 1.1795620 0.6017959 2.312024e+00 0.1651432
sdf 0.3447639 0.1752830 6.781157e-01 -1.0648955
sdi1 0.1733424 0.0386606 7.772137e-01 -1.7524866
sdi2 0.2010675 0.0431220 9.375297e-01 -1.6041144
sdi3 0.4691883 0.2028282 1.085341e+00 -0.7567511
sdc 0.5838311 0.4469616 7.626130e-01 -0.5381435
phi1 3.8811312 1.8075853 8.333316e+00 1.3561267
phi2 11.3374494 6.3114390 2.036584e+01 2.4281114
phi3 11.4590398 5.8278906 2.253124e+01 2.4387789
Deterministic reference points (Drp)
estimate cilow ciupp log.est
Bmsyd 2638.359567 1132.275821 6147.74340 7.877913
Fmsyd 2.968459 1.254387 7.02475 1.088043
MSYd 7831.863613 5312.101458 11546.85921 8.965956
Stochastic reference points (Srp)
estimate cilow ciupp log.est rel.diff.Drp
Bmsys 2434.171731 1376.685553 4303.954524 7.797362 -0.08388391
Fmsys 4.225844 2.677389 6.669839 1.441219 0.29754632
MSYs 10543.172099 5544.367419 20048.901799 9.263234 0.25716250
States w 95% CI (inp$msytype: s)
estimate cilow ciupp log.est
B_2023.94 2217.1929789 622.8802453 7892.279043 7.7039973
F_2023.94 3.2509177 1.0836816 9.752372 1.1789373
B_2023.94/Bmsy 0.9108614 0.2860864 2.900063 -0.0933646
F_2023.94/Fmsy 0.7692943 0.2321613 2.549149 -0.2622816
Predictions w 95% CI (inp$msytype: s)
prediction cilow ciupp log.est
B_2025.00 2277.6312923 395.5390189 13115.278281 7.7308913
F_2025.00 3.2509192 0.8852964 11.937782 1.1789378
B_2025.00/Bmsy 0.9356905 0.1789278 4.893129 -0.0664706
F_2025.00/Fmsy 0.7692947 0.1924187 3.075660 -0.2622812
Catch_2024.00 6150.3446013 2717.8426494 13917.928149 8.7242634
E(B_inf) 2993.6033459 NA NA 8.0042331
plot(res_sc_2)
According to the diagnostic checklist, the model meets all requirements except normality of catch residuals, as in Scenario 1.
# if 0 => OK
res_sc_2$opt$convergence
[1] 0
# if TRUE => OK
all(is.finite(res_sc_2$sd))
[1] TRUE
res_sc_2 <- calc.osa.resid(res_sc_2)
plotspict.diagnostic(res_sc_2)
# if -0.2 < mohns_rho < 0.2 => OK
retro_sc_2 <- retro(res_sc_2, nretroyear = 3)
plotspict.retro(retro_sc_2)
FFmsy BBmsy
0.009121556 0.008218423
plotspict.retro.fixed(retro_sc_2)
# if between 0.1 and 0.9 => OK
calc.bmsyk(res_sc_2)
[1] 0.4353436
plotspict.production(res_sc_2)
# Main variance paramterers (logsdb, logsdc, logsdi, logsdf) should not be unreallistically high:
get.par("logsdb", res_sc_2)
ll est ul sd cv
logsdb -0.5078369 0.1651432 0.8381233 0.3433635 2.079187
get.par("logsdc", res_sc_2)
ll est ul sd cv
logsdc -0.8052825 -0.5381435 -0.2710046 0.1362979 -0.2532742
get.par("logsdi", res_sc_2)
ll est ul sd cv
logsdi -3.252933 -1.7524866 -0.25203991 0.7655481 -0.4368353
logsdi -3.143722 -1.6041144 -0.06450683 0.7855285 -0.4896961
logsdi -1.595396 -0.7567511 0.08189393 0.4278880 -0.5654276
get.par("logsdf", res_sc_2)
ll est ul sd cv
logsdf -1.741354 -1.064895 -0.3884374 0.345138 -0.3241051
calc.om(res_sc_2) # if order of magnitude < 2 => OK)
lower est upper CI range order magnitude
B/Bmsy 0.29 0.91 2.90 2.61 1
F/Fmsy 0.06 0.21 0.73 0.67 1
check_sc_2$check.ini$resmat # Trials that converged should have same or similar estimates.
Distance m K q q q n sdb sdf sdi sdi
Basevec 0.00 7831.86 6060.41 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 1 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 2 0.13 7831.77 6060.50 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 3 7402.48 13489.36 10834.22 3.17 3.56 2.21 2.41 1.20 0.44 0.18 0.23
Trial 4 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 5 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 6 0.15 7831.80 6060.54 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 7 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 8 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 9 0.15 7831.77 6060.52 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 10 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 11 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 12 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 13 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 14 0.16 7831.78 6060.55 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 15 0.14 7831.77 6060.50 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 16 7402.53 13489.40 10834.25 3.17 3.56 2.21 2.41 1.20 0.44 0.18 0.23
Trial 17 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 18 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 19 0.69 7831.85 6061.09 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 20 0.15 7831.75 6060.51 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 21 0.07 7831.80 6060.38 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 22 0.20 7831.76 6060.57 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 23 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 24 0.12 7831.79 6060.50 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 25 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 26 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 27 0.14 7831.78 6060.52 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
Trial 28 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 29 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 30 0.14 7831.76 6060.51 6.30 8.77 5.31 1.43 1.18 0.34 0.17 0.20
sdi sdc phi phi phi
Basevec 0.47 0.58 3.88 11.34 11.46
Trial 1 NA NA NA NA NA
Trial 2 0.47 0.58 3.88 11.34 11.46
Trial 3 0.54 0.62 5.78 15.82 9.11
Trial 4 NA NA NA NA NA
Trial 5 NA NA NA NA NA
Trial 6 0.47 0.58 3.88 11.34 11.46
Trial 7 NA NA NA NA NA
Trial 8 NA NA NA NA NA
Trial 9 0.47 0.58 3.88 11.34 11.46
Trial 10 NA NA NA NA NA
Trial 11 NA NA NA NA NA
Trial 12 NA NA NA NA NA
Trial 13 NA NA NA NA NA
Trial 14 0.47 0.58 3.88 11.34 11.46
Trial 15 0.47 0.58 3.88 11.34 11.46
Trial 16 0.54 0.62 5.78 15.82 9.11
Trial 17 NA NA NA NA NA
Trial 18 NA NA NA NA NA
Trial 19 0.47 0.58 3.88 11.34 11.46
Trial 20 0.47 0.58 3.88 11.34 11.46
Trial 21 0.47 0.58 3.88 11.34 11.46
Trial 22 0.47 0.58 3.88 11.34 11.46
Trial 23 NA NA NA NA NA
Trial 24 0.47 0.58 3.88 11.34 11.46
Trial 25 NA NA NA NA NA
Trial 26 NA NA NA NA NA
Trial 27 0.47 0.58 3.88 11.34 11.46
Trial 28 NA NA NA NA NA
Trial 29 NA NA NA NA NA
Trial 30 0.47 0.58 3.88 11.34 11.46
Results when BOCADEVA data is included in the model indicate that uncertainty levels are higher. According to the estimated exploitable biomass, the model estimated an exploitable biomass of 2513.81 tonnes and a fishing mortality of 2.75. Predicted catchabilities were 5.52, 7.33, 4.45 and 8.86 for PELAGO, ECOCADIZ, ECOCADIZ-RECLUTAS and BOCADEVA, respectively. Moreover, as in the two previous scenarios, Kobe plot determines that the stock biomass is in suboptimal levels and the fishing mortality is lower than fishing mortality at MSY.
res_sc_3 <- fit.spict(sc_3_data)
summary(res_sc_3)
Convergence: 0 MSG: relative convergence (4)
Objective function at optimum: 220.8719401
Euler time step (years): 1/16 or 0.0625
Nobs C: 140, Nobs I1: 21, Nobs I2: 14, Nobs I3: 10, Nobs I4: 7
Priors
logn ~ dnorm[log(2), 2^2]
logalpha ~ dnorm[log(1), 2^2]
logbeta ~ dnorm[log(1), 2^2]
Model parameter estimates w 95% CI
estimate cilow ciupp log.est
alpha1 0.1492717 0.0284465 7.832967e-01 -1.9019871
alpha2 0.1587590 0.0458762 5.494010e-01 -1.8403677
alpha3 0.4369255 0.1556105 1.226806e+00 -0.8279926
alpha4 0.1322751 0.0261861 6.681678e-01 -2.0228717
beta 1.6438885 0.9015020 2.997630e+00 0.4970645
r 3.2607914 1.2203602 8.712805e+00 1.1819699
rc 5.0159124 2.5258119 9.960907e+00 1.6126153
rold 10.8628311 3.2319933 3.651032e+01 2.3853470
m 7589.0191643 5237.2155476 1.099691e+04 8.9344576
K 7255.2039430 3033.3622138 1.735302e+04 8.8894743
q1 5.5171546 2.1782544 1.397403e+01 1.7078623
q2 7.3343738 2.1296352 2.525927e+01 1.9925720
q3 4.4537980 1.3198273 1.502948e+01 1.4937572
q4 8.8623509 2.5640604 3.063160e+01 2.1818121
n 1.3001788 0.8470871 1.995621e+00 0.2625018
sdb 1.0674449 0.6589232 1.729243e+00 0.0652678
sdf 0.3632356 0.2025167 6.515024e-01 -1.0127036
sdi1 0.1593393 0.0352388 7.204843e-01 -1.8367192
sdi2 0.1694665 0.0549095 5.230222e-01 -1.7750998
sdi3 0.4663939 0.2135365 1.018670e+00 -0.7627247
sdi4 0.1411963 0.0300516 6.634054e-01 -1.9576039
sdc 0.5971188 0.4864066 7.330305e-01 -0.5156391
phi1 4.0426228 1.9031506 8.587234e+00 1.3968937
phi2 11.4571298 6.4915431 2.022105e+01 2.4386122
phi3 11.2542578 5.7112408 2.217702e+01 2.4207465
Deterministic reference points (Drp)
estimate cilow ciupp log.est
Bmsyd 3025.977578 1375.735821 6655.740271 8.0149895
Fmsyd 2.507956 1.262906 4.980453 0.9194682
MSYd 7589.019164 5237.215548 10996.914554 8.9344576
Stochastic reference points (Srp)
estimate cilow ciupp log.est rel.diff.Drp
Bmsys 2373.58976 298.3070405 18886.34053 7.772159 -0.2748528
Fmsys 4.50693 0.9803418 20.71973 1.505616 0.4435334
MSYs 12001.70964 3424.8397796 42057.74390 9.392804 0.3676718
States w 95% CI (inp$msytype: s)
estimate cilow ciupp log.est
B_2023.94 2513.8097970 695.7095338 9083.158112 7.8295547
F_2023.94 2.7535907 0.9059081 8.369791 1.0129058
B_2023.94/Bmsy 1.0590751 0.0597899 18.759704 0.0573960
F_2023.94/Fmsy 0.6109681 0.0562845 6.632060 -0.4927105
Predictions w 95% CI (inp$msytype: s)
prediction cilow ciupp log.est
B_2025.00 2568.9390262 457.4582694 14426.338228 7.8512483
F_2025.00 2.7535921 0.7267465 10.433170 1.0129063
B_2025.00/Bmsy 1.0823012 0.0492211 23.798247 0.0790895
F_2025.00/Fmsy 0.6109684 0.0504034 7.405899 -0.4927100
Catch_2024.00 6120.2249729 2689.7488405 13925.892691 8.7193541
E(B_inf) 4158.0545086 NA NA 8.3328026
plot(res_sc_3)
In relation to the diagnostic checklist, the model meets all requirements except normality of catch residuals and order of magnitudes of B/BMSY and F/FMSY. In this sense, B/BMSY and F/FMSY orders of magnitude were 3 and 2, respectively. Moreover, the retrospective analysis could not converge with peel -3.
# if 0 => OK
res_sc_3$opt$convergence
[1] 0
# if TRUE => OK
all(is.finite(res_sc_3$sd))
[1] TRUE
res_sc_3 <- calc.osa.resid(res_sc_3)
plotspict.diagnostic(res_sc_3)
# if -0.2 < mohns_rho < 0.2 => OK
retro_sc_3 <- retro(res_sc_3, nretroyear = 3)
Error in calc.osa.resid(rep) :
Could not calculate OSA residuals because estimation did not converge.
plotspict.retro(retro_sc_3)
Excluded 1 retrospective runs that was not converged: 3
FFmsy BBmsy
0.07179524 -0.01381742
plotspict.retro.fixed(retro_sc_3)
Excluded 1 retrospective run that was not converged: 3
# if between 0.1 and 0.9 => OK
calc.bmsyk(res_sc_3)
[1] 0.4170768
plotspict.production(res_sc_3)
# Main variance paramterers (logsdb, logsdc, logsdi, logsdf) should not be unreallistically high:
get.par("logsdb", res_sc_3)
ll est ul sd cv
logsdb -0.4171483 0.06526784 0.547684 0.2461352 3.771156
get.par("logsdc", res_sc_3)
ll est ul sd cv
logsdc -0.7207103 -0.5156391 -0.3105679 0.1046301 -0.2029134
get.par("logsdi", res_sc_3)
ll est ul sd cv
logsdi -3.345607 -1.8367192 -0.32783169 0.7698547 -0.4191467
logsdi -2.902068 -1.7750998 -0.64813136 0.5749945 -0.3239223
logsdi -1.543948 -0.7627247 0.01849811 0.3985904 -0.5225875
logsdi -3.504839 -1.9576039 -0.41036897 0.7894201 -0.4032583
get.par("logsdf", res_sc_3)
ll est ul sd cv
logsdf -1.596933 -1.012704 -0.4284742 0.2980817 -0.2943425
calc.om(res_sc_3) # if order of magnitude < 2 => OK)
lower est upper CI range order magnitude
B/Bmsy 0.06 1.06 18.76 18.70 3
F/Fmsy 0.01 0.16 1.87 1.85 2
check_sc_3$check.ini$resmat # Trials that converged should have same or similar estimates.
Distance m K q q q q n sdb sdf sdi sdi
Basevec 0.00 7589.02 7255.20 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 1 0.05 7589.06 7255.17 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 2 0.16 7589.01 7255.04 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 3 0.07 7588.96 7255.18 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 4 0.00 7589.02 7255.20 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 5 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 6 0.03 7589.04 7255.19 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 7 0.01 7589.02 7255.19 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 8 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 9 0.00 7589.02 7255.21 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 10 0.00 7589.02 7255.20 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 11 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 12 0.07 7589.01 7255.28 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 13 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 14 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 15 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 16 0.02 7589.04 7255.19 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 17 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 18 0.01 7589.02 7255.22 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 19 0.01 7589.02 7255.21 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 20 0.20 7589.04 7255.40 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 21 0.05 7589.04 7255.25 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 22 0.01 7589.02 7255.21 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 23 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 24 0.17 7589.12 7255.33 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 25 0.12 7588.90 7255.17 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 26 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 27 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 28 0.07 7589.00 7255.14 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
Trial 29 0.00 NA NA NA NA NA NA NA NA NA NA NA
Trial 30 0.06 7589.07 7255.24 5.52 7.33 4.45 8.86 1.3 1.07 0.36 0.16 0.17
sdi sdi sdc phi phi phi
Basevec 0.47 0.14 0.6 4.04 11.46 11.25
Trial 1 0.47 0.14 0.6 4.04 11.46 11.25
Trial 2 0.47 0.14 0.6 4.04 11.46 11.25
Trial 3 0.47 0.14 0.6 4.04 11.46 11.25
Trial 4 0.47 0.14 0.6 4.04 11.46 11.25
Trial 5 NA NA NA NA NA NA
Trial 6 0.47 0.14 0.6 4.04 11.46 11.25
Trial 7 0.47 0.14 0.6 4.04 11.46 11.25
Trial 8 NA NA NA NA NA NA
Trial 9 0.47 0.14 0.6 4.04 11.46 11.25
Trial 10 0.47 0.14 0.6 4.04 11.46 11.25
Trial 11 NA NA NA NA NA NA
Trial 12 0.47 0.14 0.6 4.04 11.46 11.25
Trial 13 NA NA NA NA NA NA
Trial 14 NA NA NA NA NA NA
Trial 15 NA NA NA NA NA NA
Trial 16 0.47 0.14 0.6 4.04 11.46 11.25
Trial 17 NA NA NA NA NA NA
Trial 18 0.47 0.14 0.6 4.04 11.46 11.25
Trial 19 0.47 0.14 0.6 4.04 11.46 11.25
Trial 20 0.47 0.14 0.6 4.04 11.46 11.25
Trial 21 0.47 0.14 0.6 4.04 11.46 11.25
Trial 22 0.47 0.14 0.6 4.04 11.46 11.25
Trial 23 NA NA NA NA NA NA
Trial 24 0.47 0.14 0.6 4.04 11.46 11.25
Trial 25 0.47 0.14 0.6 4.04 11.46 11.25
Trial 26 NA NA NA NA NA NA
Trial 27 NA NA NA NA NA NA
Trial 28 0.47 0.14 0.6 4.04 11.46 11.25
Trial 29 NA NA NA NA NA NA
Trial 30 0.47 0.14 0.6 4.04 11.46 11.25
Results when BOCADEVA data and it’s uncertainty levels were included in the model also show high uncertainty levels of model estimations. According to the estimated exploitable biomass, the model estimated an exploitable biomass of 2440.36 tonnes and a fishing mortality of 2.84. Predicted catchabilities were 5.62, 7.55, 4.58 and 9.08 for PELAGO, ECOCADIZ, ECOCADIZ-RECLUTAS and BOCADEVA, respectively. Kobe plot again defines stock biomass in suboptimal levels and the fishing mortality in lower levels than fishing mortality at MSY.
res_sc_4 <- fit.spict(sc_4_data)
summary(res_sc_4)
Convergence: 0 MSG: relative convergence (4)
Objective function at optimum: 220.42738
Euler time step (years): 1/16 or 0.0625
Nobs C: 140, Nobs I1: 21, Nobs I2: 14, Nobs I3: 10, Nobs I4: 7
Priors
logn ~ dnorm[log(2), 2^2]
logalpha ~ dnorm[log(1), 2^2]
logbeta ~ dnorm[log(1), 2^2]
Model parameter estimates w 95% CI
estimate cilow ciupp log.est
alpha1 0.1475009 0.0280514 7.755940e-01 -1.9139211
alpha2 0.1420530 0.0348073 5.797358e-01 -1.9515554
alpha3 0.4267707 0.1500109 1.214133e+00 -0.8515084
alpha4 0.2502797 0.0532823 1.175625e+00 -1.3851760
beta 1.6429756 0.9033329 2.988233e+00 0.4965090
r 3.3269404 1.2402004 8.924794e+00 1.2020531
rc 5.1036921 2.5632432 1.016200e+01 1.6299642
rold 10.9532980 3.3277610 3.605269e+01 2.3936406
m 7587.9997108 5250.7683247 1.096558e+04 8.9343233
K 7120.5758661 2979.0337578 1.701981e+04 8.8707439
q1 5.6240096 2.2269346 1.420315e+01 1.7270449
q2 7.5484102 2.1855796 2.607020e+01 2.0213370
q3 4.5753142 1.3554758 1.544365e+01 1.5206754
q4 9.0768836 2.6402405 3.120542e+01 2.2057309
n 1.3037387 0.8497476 2.000282e+00 0.2652361
sdb 1.0813576 0.6654948 1.757090e+00 0.0782173
sdf 0.3624569 0.2023657 6.491961e-01 -1.0148497
sdi1 0.1595012 0.0352494 7.217332e-01 -1.8357038
sdi2 0.1536100 0.0420065 5.617230e-01 -1.8733381
sdi3 0.4614917 0.2089301 1.019358e+00 -0.7732912
sdi4 0.2706419 0.0617029 1.187093e+00 -1.3069587
sdc 0.5955078 0.4836252 7.332735e-01 -0.5183407
phi1 4.0102790 1.8849893 8.531793e+00 1.3888608
phi2 11.3755379 6.4251603 2.014002e+01 2.4314653
phi3 11.3034089 5.7492505 2.222325e+01 2.4251043
Deterministic reference points (Drp)
estimate cilow ciupp log.est
Bmsyd 2973.533507 1354.117240 6529.642530 7.997506
Fmsyd 2.551846 1.281622 5.080999 0.936817
MSYd 7587.999711 5250.768325 10965.583711 8.934323
Stochastic reference points (Srp)
estimate cilow ciupp log.est rel.diff.Drp
Bmsys 2474.098015 603.346879 10145.34292 7.813631 -0.2018657
Fmsys 4.361733 1.263216 15.06054 1.472870 0.4149468
MSYs 11695.277848 3760.353370 36374.11447 9.366940 0.3511912
States w 95% CI (inp$msytype: s)
estimate cilow ciupp log.est
B_2023.94 2440.3596567 676.2632860 8806.267289 7.7999007
F_2023.94 2.8432254 0.9420090 8.581585 1.0449391
B_2023.94/Bmsy 0.9863634 0.1067080 9.117520 -0.0137305
F_2023.94/Fmsy 0.6518568 0.0793537 5.354723 -0.4279304
Predictions w 95% CI (inp$msytype: s)
prediction cilow ciupp log.est
B_2025.00 2485.4785739 436.3576031 14157.204316 7.8182205
F_2025.00 2.8432268 0.7554802 10.700397 1.0449396
B_2025.00/Bmsy 1.0045999 0.0818640 12.328024 0.0045893
F_2025.00/Fmsy 0.6518571 0.0701220 6.059692 -0.4279299
Catch_2024.00 6097.2496810 2672.7504817 13909.436712 8.7155931
E(B_inf) 3921.6866360 NA NA 8.2742771
plot(res_sc_4)
Diagnostic checklist determined that model met all requirements except normality of catch residuals and order of magnitude of F/FMSY. In this sense, F/FMSY order of magnitude was 2. Additionally, the retrospective analysis could not converge with peel -3.
# if 0 => OK
res_sc_4$opt$convergence
[1] 0
# if TRUE => OK
all(is.finite(res_sc_4$sd))
[1] TRUE
res_sc_4 <- calc.osa.resid(res_sc_4)
plotspict.diagnostic(res_sc_4)
# if -0.2 < mohns_rho < 0.2 => OK
retro_sc_4 <- retro(res_sc_4, nretroyear = 3)
Error in calc.osa.resid(rep) :
Could not calculate OSA residuals because estimation did not converge.
plotspict.retro(retro_sc_4)
Excluded 1 retrospective runs that was not converged: 3
FFmsy BBmsy
0.06803314 -0.02170606
plotspict.retro.fixed(retro_sc_4)
Excluded 1 retrospective run that was not converged: 3
# if between 0.1 and 0.9 => OK
calc.bmsyk(res_sc_4)
[1] 0.4175973
plotspict.production(res_sc_4)
# Main variance paramterers (logsdb, logsdc, logsdi, logsdf) should not be unreallistically high:
get.par("logsdb", res_sc_4)
ll est ul sd cv
logsdb -0.4072245 0.07821726 0.563659 0.2476789 3.166551
get.par("logsdc", res_sc_4)
ll est ul sd cv
logsdc -0.726445 -0.5183407 -0.3102365 0.1061776 -0.2048413
get.par("logsdi", res_sc_4)
ll est ul sd cv
logsdi -3.345308 -1.8357038 -0.32609972 0.7702203 -0.4195777
logsdi -3.169930 -1.8733381 -0.57674647 0.6615385 -0.3531335
logsdi -1.565755 -0.7732912 0.01917312 0.4043259 -0.5228638
logsdi -2.785425 -1.3069587 0.17150763 0.7543334 -0.5771670
get.par("logsdf", res_sc_4)
ll est ul sd cv
logsdf -1.597679 -1.01485 -0.4320205 0.2973673 -0.2930161
calc.om(res_sc_4) # if order of magnitude < 2 => OK)
lower est upper CI range order magnitude
B/Bmsy 0.11 0.99 9.12 9.01 1
F/Fmsy 0.02 0.18 1.52 1.50 2
check_sc_4$check.ini$resmat # Trials that converged should have same or similar estimates.
Distance m K q q q q n sdb sdf sdi
Basevec 0.00 7588.00 7120.58 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 1 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 2 0.10 7588.01 7120.67 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 3 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 4 0.11 7588.10 7120.63 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 5 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 6 0.29 7588.06 7120.86 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 7 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 8 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 9 0.09 7588.00 7120.67 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 10 0.10 7588.02 7120.67 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 11 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 12 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 13 0.10 7588.02 7120.67 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 14 0.11 7588.02 7120.68 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 15 0.15 7588.01 7120.73 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 16 0.09 7588.02 7120.67 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 17 11314.44 12366.28 17376.53 2.00 2.14 1.34 2.58 1.68 0.99 0.41 0.15
Trial 18 0.10 7588.02 7120.67 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 19 0.12 7588.00 7120.70 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 20 0.09 7588.00 7120.66 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 21 0.13 7588.02 7120.71 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 22 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 23 11314.44 12366.22 17376.55 2.00 2.14 1.34 2.58 1.68 0.99 0.41 0.15
Trial 24 11314.30 12366.20 17376.40 2.00 2.14 1.34 2.58 1.68 0.99 0.41 0.15
Trial 25 11314.38 12366.29 17376.46 2.00 2.14 1.34 2.58 1.68 0.99 0.41 0.15
Trial 26 0.08 7588.01 7120.66 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 27 0.12 7588.01 7120.69 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 28 0.00 NA NA NA NA NA NA NA NA NA NA
Trial 29 0.10 7588.02 7120.67 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
Trial 30 0.06 7587.98 7120.63 5.62 7.55 4.58 9.08 1.30 1.08 0.36 0.16
sdi sdi sdi sdc phi phi phi
Basevec 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 1 NA NA NA NA NA NA NA
Trial 2 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 3 NA NA NA NA NA NA NA
Trial 4 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 5 NA NA NA NA NA NA NA
Trial 6 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 7 NA NA NA NA NA NA NA
Trial 8 NA NA NA NA NA NA NA
Trial 9 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 10 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 11 NA NA NA NA NA NA NA
Trial 12 NA NA NA NA NA NA NA
Trial 13 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 14 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 15 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 16 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 17 0.17 0.50 0.25 0.65 5.90 15.00 8.43
Trial 18 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 19 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 20 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 21 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 22 NA NA NA NA NA NA NA
Trial 23 0.17 0.50 0.25 0.65 5.90 15.00 8.43
Trial 24 0.17 0.50 0.25 0.65 5.90 15.00 8.43
Trial 25 0.17 0.50 0.25 0.65 5.90 15.00 8.43
Trial 26 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 27 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 28 NA NA NA NA NA NA NA
Trial 29 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Trial 30 0.15 0.46 0.27 0.60 4.01 11.38 11.30
Results indicate that the most robust scenario was Scenario 2. This scenario obtained better results than Scenario 3 and 4 in the diagnostic checklist and included an uncertainty level in ECOCADIZ-RECLUTAS 2012 estimate, making it more realistic than Scenario 1. The greater robustness shown by Scenario 2 compared to Scenarios 3 and 4 could be due to the number of estimates from the BOCADEVA campaign (7 estimates). The low number of estimates from BOCADEVA index may have negatively affected the model fit introducing some noise or additional uncertainty. Thus, we recommend using the scenario 2 estimates over the other scenarios. Finally, a larger number of estimates in the BOCADEVA survey could improve the model obtained in both scenario 3 and scenario 4. Therefore, in order to define the influence of BOCADEVA estimates in the model, we recommend repeating the same exercise in a few years when more BOCADEVA campaigns have been carried out.
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